aiqbench analysis
This research addresses critical vulnerabilities in generative audio models by introducing HIR-SDD, a framework that leverages Large Audio Language Models and chain-of-thought reasoning to enhance deepfake detection generalization and interpretability. By integrating human-inspired reasoning patterns, the system provides transparent justifications for its classifications, directly tackling the lack of explainability in current speech spoofing defenses. The work represents a significant step toward robust AI safety mechanisms capable of withstanding evolving adversarial attacks in audio domains.
Towards Robust Speech Deepfake Detection via Human-Inspired Reasoning
arXiv:2603.10725v4 Announce Type: replace-cross Abstract: The modern generative audio models can be used by an adversary in an unlawful manner, specifically, to impersonate other people to gain access to private information. To mitigate this issue, speech deepfake detection (SDD) methods started to evolve. Unfortunately, current SDD methods generally suffer from the lack of generalization to new audio domains and generators. More than that, they lack interpretability, especially human-like reasoning that would naturally explain the attribution of a given audio to the bona fide or spoof class and provide human-perceptible cues. In this paper, we propose HIR-SDD, a novel SDD framework that combines the strengths of Large Audio Language Models (LALMs) with the chain-of-thought reasoning derived from the novel proposed human-annotated dataset. Experimental evaluation demonstrates both the effectiveness of the proposed method and its ability to provide reasonable justifications for predictions.
Abstract:The modern generative audio models can be used by an adversary in an unlawful manner, specifically, to impersonate other people to gain access to private information. To mitigate this issue, speech deepfake detection (SDD) methods started to evolve. Unfortunately, current SDD methods generally suffer from the lack of generalization to new audio domains and generators. More than that, they lack interpretability, especially human-like reasoning that would naturally explain the attribution of a given audio to the bona fide or spoof class and provide human-perceptible cues. In this paper, we propose HIR-SDD, a novel SDD framework that combines the strengths of Large Audio Language Models (LALMs) with the chain-of-thought reasoning derived from the novel proposed human-annotated dataset. Experimental evaluation demonstrates both the effectiveness of the proposed method and its ability to provide reasonable justifications for predictions.
Sound (cs.SD); Artificial Intelligence (cs.AI)
arXiv:2603.10725 [cs.SD]
(or arXiv:2603.10725v4 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2603.10725 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.21437/Interspeech.2026-1289 Focus to learn more DOI(s) linking to related resources
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